Shield Yourself Against Payment Frauds Using AI/ML Models

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Scammers exist in all forms of commerce. With the advancement of e-commerce, fraud has taken on new forms and become more powerful than ever before. Fraudsters take full advantage of any loophole in any system.

Preventing, detecting, and eliminating fraud is one of the major focus areas of the e-commerce and banking industries at present. Banks and other financial institutions are investing in new ways to meet the challenge of preventing fraud.

Firms are now embracing Artificial Intelligence (AI) and Machine Learning (ML) technology to detect, investigate, and reduce money laundering and transaction fraud effectively and efficiently.

AI-based fraud prevention is very effective at reducing chargebacks, fake accounts, spam, account takeovers and so on.

What is the need for AI/ML for payment fraud detection?

 Fighting against payment fraud by using AI/ML models is more efficient compared to manual and automated rule-based fraud detection, reason being:

  • Effective interpretation of large datasets: Larger data is needed to get better insight to customer behavior and their preferences. For larger datasets, AI/ML models are more effective compared to humans because they have better computational capacity.
  • Automation in preventing fraud: Automation is needed to prevent fraud attempts. AI/ML models analyzes large dataset and can automatically reject a transaction if the data is indicated fraudulent. Technology brings accurate results.
  • Real-time fraud analysis: Real-time transactions need real-time fraud analysis. It acts fast to assess users’ behavior and make real-time decisions. Artificial Intelligence and Machine learning are efficient techniques to do data analysis in less time.
  • Reducing False positives: using rule-based systems, legitimate customers are flagged as bad actors and their card-not-present (CNP) transactions are incorrectly flagged as fraudulent. This ultimately prevented legitimate customers from completing their orders. AI/ML reduces these false positives.
  • Fraud undetectable in complex systems: Payments fraud-based attacks are growing in complexity and often have a different digital footprint or pattern, sequence, and structure, which makes them undetectable using rules-based logic and predictive models alone.
  • Lowering costs of fraud prevention: Along with the reduction in fraud-related monetary losses, which is the main advantage, AI and ML technologies reduce the costs associated with manual fraud investigations and increase customer satisfaction. Risk scoring and fraud prevention solutions for financial institutions bring significant returns on investment.

How does AI/ML detect payment fraud?

Fraudulent transactions have specific features that legitimate transactions do not have. Based on this concept, machine learning algorithms detect patterns in financial operations and decide whether a given transaction is legitimate.

  • A machine learning model first collects data and then analyzes it. Analyzing the data includes determining the amount of money customers spend, where they spend it, what goods or services they tend to buy, the places where they make transactions, and so on.
  • Then, features are extracted from the data. Features describing good customer behavior and fraudulent behavior are added. These features usually include the customer’s location, identity, orders, network, and chosen payment method.
  • Next, a training algorithm is launched. This algorithm is a set of rules that the ML model must follow when deciding whether an operation is legitimate or fraudulent.

The algorithms can be of the following types:

  • Supervised learning – here all input information must be labeled as good or bad. It is based on predictive data analysis and is only as accurate as the training set provided for it. A major drawback of the model is that it’s not able to detect fraud that was not included in the historical data set.
  • Unsupervised learning – is meant to detect anomalous behavior in cases where there is little transaction data. An unsupervised learning model continuously processes and analyzes new data and updates its models based on the findings.
  • Semi-supervised learning – works for cases where human experts are required for labeling information. It stores a small number of labeled examples and a large number of unlabeled examples.
  • Reinforcement learning – is a feedback-based Machine learning technique. It constantly learns from the environment to find actions that minimize risks and maximize rewards.

The more data a business can provide for a training set, the better the ML model will be. If there is an unusual pattern in customer spending, the business can notify the customer and ask for further authentication to continue the purchase or decline the transaction if the calculated risk is very high. This process is called “data scoring”.

Along with the determination of a transaction’s legitimacy, whether a device is legitimate or not can be determined using device intelligence. AI can determine the device profile within a span of milliseconds and help banks stop fraud before it occurs. Information can be collected from devices based on cookies and web beacons as soon as a customer visits a bank’s website. Attribute risks for a device are determined by the AI model, confirming geolocation mismatches and determining device type, OS, and screen resolution mismatches.
The AI solution finds whether a device, which the customer has utilized, has an abnormally high amount of online activity and, along with other factors, uses it in real time to determine fraud patterns and recommend whether a transaction needs to be denied, approved, or reviewed.

Maintaining and upgrading AI ML solutions 

  • Effective data cleaning: The efficiency of ML models depends on the size and quality of the datasets. Hence, it is important to perform effective data cleaning.
  • Data Versioning: It is important to store data from different times to version the data. Data versioning can be an effective tool for prioritizing new fraud techniques. By deleting old historical data that is no longer relevant, organizations can more effectively deploy resources against current fraud trends.
  • Adaptive analytics which is a form of predictive analytics that captures and analyzes real-time data rather than historical data.

A machine learning model needs to be constantly improved and updated to be effective in credit card fraud detection. Otherwise, fraudsters will come up with new tricks to penetrate the system. Rules-based fraud systems and basic supervised machine learning are no longer enough to keep pace with the evolving sophistication of fraud and cybercrime. The need for intelligent fraud detection using next-generation artificial intelligence has become necessary now.

Companies like Tookitaki, Feedzai, Actimize, BAE Systems NetReveal®, NoFraud, SignifyD, Iovation, Simility, and the list continues, have financial crime prevention AI/ML solutions to iron out and control sophisticated cyber-attacks.

Companies that are already using an analytical approach for fraud prevention have reported several important benefits, but there are some organizations that have held themselves back because of the seeming complexity. However, there are solutions emerging that offer stepwise integration, with intermediary steps designed to bridge the gap between traditional systems and next generation technology so that financial institutions don’t have to jump straight into the most complex forms of AI and ML.

Cigniti Technologies is set to acquire Aparaa Digital, a leading AI/ML, data engineering, and analytics services company that operates under the brand name RoundSqr. Cigniti, along with Aparaa, brings consulting-driven expertise, digital engineering, transformation, and assurance covering AI and ML capabilities.

Critical AI operational challenges and ML model validation challenges are solved using Zastra, an active learning-AI based data annotation and MLOps platform.

Need help? Talk to our AI ML experts to learn more about ML landscape and strategy support, model building, data analysis, annotations, and assurance, and how Cigniti can help in the AI ML digital transformation journey.

Author

  • Payel Ghosh has 18 years of experience in diverse technology projects in the banking and payments domain. She has rich experience and knowledge in the payments value chain and in-depth subject matter expertise in payment processing. Payel is a consultant with Cigniti's BFSI Practice and Centers of Excellence that focuses on building deep domain competence and developing solutions for the challenges faced by the industry.

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